//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************

#pragma once

#include "ngraph/autodiff/adjoints.hpp"
#include "ngraph/node.hpp"
#include "ngraph/op/util/attr_types.hpp"
#include "ngraph/op/util/fused_op.hpp"

namespace ngraph
{
    namespace op
    {
        ///
        /// \brief      Class performing element-wise linear quantization.
        ///
        /// \note       Input floating point values are quantized into a discrete
        ///             set of floating point values.
        ///
        /// \paragraph Implementation This class creates a node which performs the following
        ///            operation:
        ///
        ///            round((data - input_low) / (input_high - input_low) * (levels-1)) /
        ///                 (levels-1) * (output_high - output_low) + output_low
        ///
        ///
        class FakeQuantize : public ngraph::op::util::FusedOp
        {
        public:
            NGRAPH_API
            static constexpr NodeTypeInfo type_info{"FakeQuantize", 0};
            const NodeTypeInfo& get_type_info() const override { return type_info; }
            FakeQuantize() = default;
            ///
            /// \brief      Constructs a FakeQuantize operation node.
            ///
            /// \param[in]  data            The input data tensor.
            /// \param[in]  input_low       The minimum limit for input values.
            /// \param[in]  input_high      The maximum limit for input values.
            /// \param[in]  output_low      The minimum quantized value.
            /// \param[in]  output_high     The maximum quantized value.
            /// \param[in]  levels          The number of quantization levels.
            /// \param[in]  auto_broadcast  AutoBroadcast mode to be used for broadcasting
            ///                             limit values
            ///
            FakeQuantize(const Output<Node>& data,
                         const Output<Node>& input_low,
                         const Output<Node>& input_high,
                         const Output<Node>& output_low,
                         const Output<Node>& output_high,
                         std::size_t levels,
                         const AutoBroadcastSpec& auto_broadcast =
                             AutoBroadcastSpec(AutoBroadcastType::NUMPY));

            virtual NodeVector decompose_op() const override;
            virtual void pre_validate_and_infer_types() override;

            virtual std::shared_ptr<Node>
                copy_with_new_args(const NodeVector& new_args) const override;

            std::size_t get_levels() const { return m_levels; }
            void set_levels(std::size_t levels) { m_levels = levels; }
            const AutoBroadcastSpec& get_auto_broadcast() const { return m_auto_broadcast; }
            void set_auto_broadcast(const AutoBroadcastSpec& auto_broadcast)
            {
                m_auto_broadcast = auto_broadcast;
            }

        private:
            std::size_t m_levels;
            AutoBroadcastSpec m_auto_broadcast;
        };
    }
}
